mlsanityData to 7 Oct 2026

advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch

arXiv 1902.07623 ↗First posted 20 Feb 2019cs.LG cs.CR cs.CVcited by 69

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Abstract (arXiv)

advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch is built on PyTorch (Paszke et al., 2017), and leverages the advantages of the dynamic computational graph to provide concise and efficient reference implementations. The code is licensed under the LGPL license and is open sourced at https://github.com/BorealisAI/advertorch .

Key facts. Cited by 69 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Feb 2019.

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Citing papers per year: 2021: 25; 2022: 13; 2023: 11; 2024: 8; 2025: 11; 2026: 1.

69 papers.

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  1. 2021Trustworthy AI: A Computational Perspectivecited by 93
  2. 2021Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraintscited by 50
  3. 2022Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?cited by 47
  4. 2021Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Datacited by 45
  5. 2021PAC Prediction Sets Under Covariate Shiftcited by 42
  6. 2025Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignmentcited by 42
  7. 2024One Perturbation is Enough: On Generating Universal Adversarial Perturbations against Vision-Language Pre-training Modelscited by 39
  8. 2021Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networkscited by 29
  9. 2021Understanding the Dynamics of DNNs Using Graph Modularitycited by 26
  10. 2021Towards Defending against Adversarial Examples via Attack-Invariant Featurescited by 25
  11. 2023Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspectivecited by 19
  12. 2024CLIP-Guided Generative Networks for Transferable Targeted Adversarial Attackscited by 19
38 more
  1. 2021Removing Adversarial Noise in Class Activation Feature Spacecited by 18
  2. 2022On the Effectiveness of Adversarial Training against Backdoor Attackscited by 15
  3. 2021TOP: Backdoor Detection in Neural Networks via Transferability of Perturbationcited by 13
  4. 2021Is RobustBench/AutoAttack a suitable Benchmark for Adversarial Robustness?cited by 12
  5. 2021DI-AA: An Interpretable White-box Attack for Fooling Deep Neural Networkscited by 10
  6. 2021Detecting AutoAttack Perturbations in the Frequency Domaincited by 10
  7. 2024Dormant: Defending against Pose-driven Human Image Animationcited by 9
  8. 2021Discriminator-Free Generative Adversarial Attackcited by 8
  9. 2022Exploring Adversarially Robust Training for Unsupervised Domain Adaptationcited by 8
  10. 2021Performance Evaluation of Adversarial Attacks: Discrepancies and Solutionscited by 7
  11. 2021Instance Correction for Learning with Open-set Noisy Labelscited by 7
  12. 2021Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attackscited by 7
  13. 2021Graph-Based Similarity of Neural Network Representationscited by 7
  14. 2021Adversarial Learning with Cost-Sensitive Classescited by 6
  15. 2021Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Modelscited by 6
  16. 2022Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networkscited by 6
  17. 2022Towards Out-of-Distribution Adversarial Robustnesscited by 5
  18. 2024Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attackscited by 5
  19. 2021Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptionscited by 4
  20. 2022Human Eyes Inspired Recurrent Neural Networks are More Robust Against Adversarial Noisescited by 4
  21. 2024On Robust Reinforcement Learning with Lipschitz-Bounded Policy Networkscited by 4
  22. 2025PhysPatch: A Physically Realizable and Transferable Adversarial Patch Attack for Multimodal Large Language Models-based Autonomous Driving Systemscited by 4
  23. 2021SEC4SR: A Security Analysis Platform for Speaker Recognitioncited by 3
  24. 2022VITA: A Multi-Source Vicinal Transfer Augmentation Method for Out-of-Distribution Generalizationcited by 3
  25. 2023URET: Universal Robustness Evaluation Toolkit (for Evasion)cited by 3
  26. 2025On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigmscited by 3
  27. 2021IWA: Integrated Gradient based White-box Attacks for Fooling Deep Neural Networkscited by 2
  28. 2021Analytically Tractable Hidden-States Inference in Bayesian Neural Networkscited by 2
  29. 2021Saliency Diversified Deep Ensemble for Robustness to Adversariescited by 2
  30. 2022Robust Sensible Adversarial Learning of Deep Neural Networks for Image Classificationcited by 2
  31. 2022Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member Modelscited by 2
  32. 2023Computational Asymmetries in Robust Classificationcited by 2
  33. 2023Rethinking PGD Attack: Is Sign Function Necessary?cited by 2
  34. 2024Improving Adversarial Robustness via Decoupled Visual Representation Maskingcited by 2
  35. 2023Single-Class Target-Specific Attack against Interpretable Deep Learning Systemscited by 1
  36. 2023Microbial Genetic Algorithm-based Black-box Attack against Interpretable Deep Learning Systemscited by 1
  37. 2023Intrinsic Biologically Plausible Adversarial Robustnesscited by 1
  38. 2025Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via In-the-wild Cascading Flow Optimizationcited by 1

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